Currently, traditional search schemes are mainly as follows. All relevant documents are searched in the network according to the information to be searched, which is input by a user. The relevancy degree of each relevant document to the information to be searched is calculated based on a certain algorithm rule. All the relevant documents are sorted based on the value of the relevancy degree, and the sorting result is returned to the user as a search result. It can be seen from the above that the value of the relevancy degree, which is generally reflected by a correlation score intuitionally, affects the sorting result of the relevant documents directly, and affects the search result of the user directly.
In the traditional search schemes, normally, the correlation is calculated through a word matching algorithm. For example, the correlation is scored by an algorithm such as the BM25 (Best Match) algorithm, the proximity (term proximity scoring) algorithm, or the like. The larger the correlation score, the higher the relevancy degree. A description is made as follows, taking a search scheme based on the BM25 algorithm as an example. Assume that the information to be searched, which is input by the user, is “capital of China”. According to the correlation score rule of the BM25 algorithm, a relevant document can have a corresponding correlation score only when the words “China” and “capital” appear in the relevant document. Otherwise, the correlation score of the relevant document is 0. For example, one of the relevant documents contains “Beijing, which is a famous historical and cultural city with a city history of more than 3,000 years and a history of more than 850 years as the capital, is the political and cultural center of the country, and is also the biggest land and air production/transportation hub in the country”. According to the above traditional search schemes, the correlation score of the relevant document is 0, indicating that it is irrelevant to the information to be searched. However, from the viewpoint of semantic relationship, the correlation between the relevant document and the information to be searched is very high. After the sorting process, the relevant document may be ordered in a previous search result page, which is disadvantageous for the user to check. It can be known from the above example that the traditional search scheme performs correlation matching based only on them, and does not consider the semantic relationship between words, which may result in an inaccurate calculation result of the correlation, thereby affecting the order of the search result, decreasing the satisfaction of the user with the search result and deteriorating the user's search experience.